from_pretrained(‘bert-base-uncased‘) 报错_model name 'bert-base-uncased' was not found in mo

OSError: Model name ‘bert-base-cased’ was not found in tokenizers model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, TurkuNLP/bert-base-finnish-cased-v1, TurkuNLP/bert-base-finnish-uncased-v1, wietsedv/bert-base-dutch-cased). We assumed ‘bert-base-cased’ was a path, a model identifier, or url to a directory containing vocabulary files named [‘vocab.txt’] but couldn’t find such vocabulary files at this path or url.

在huggingface上把bert模型下载下来，放到一个文件夹里，就叫bert-base-uncased，'bert-base-uncased’改为"./bert-base-uncased"，解决

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained(‘./bert-base-uncased’)
model = BertModel.from_pretrained(“./bert-base-uncased”)
text = “Replace me by any text you’d like.”
encoded_input = tokenizer(text, return_tensors=‘pt’)
output = model(**encoded_input)
print(output)